Advances in Research and Applications of Co2-Based Demand-Controlled Ventilation in Commercial Buildings: A Critical Review of Control Strategies and Performance Evaluation

[1]  B. Dong,et al.  From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control , 2021, Building Simulation.

[2]  Zhang Lin,et al.  Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving , 2021, Applied Energy.

[3]  Bart Merema,et al.  Analysing modelling challenges of smart controlled ventilation systems in educational buildings , 2021 .

[4]  Hwataik Han,et al.  Real-time ventilation control based on a Bayesian estimation of occupancy , 2021, Building Simulation.

[5]  Zheng O’Neill,et al.  Energy and ventilation performance analysis for CO2-based demand-controlled ventilation in multiple-zone VAV systems with fan-powered terminal units (ASHRAE RP-1819) , 2020 .

[6]  Shengwei Wang,et al.  A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use , 2020 .

[7]  Stuart B. Dalziel,et al.  Effects of ventilation on the indoor spread of COVID-19 , 2020, Journal of Fluid Mechanics.

[8]  T. Zhao,et al.  Comparative study of outdoor airflow requirement and distribution in multi-zone VAV system with different control strategies , 2020 .

[9]  A. Franco,et al.  Definition of Optimal Ventilation Rates for Balancing Comfort and Energy Use in Indoor Spaces Using CO2 Concentration Data , 2020, Buildings.

[10]  Gary Higgins,et al.  Evaluation of real-life demand-controlled ventilation from the perception of indoor air quality with probable implications , 2020 .

[11]  Zhe Wang,et al.  Reinforcement learning for building controls: The opportunities and challenges , 2020, Applied Energy.

[12]  Can Cui,et al.  An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model , 2020, Applied Energy.

[13]  Fuxin Niu,et al.  A novel simulation-based framework for sensor error impact analysis in smart building systems: A case study for a demand-controlled ventilation system , 2020 .

[14]  Steven T. Taylor,et al.  Energy savings and ventilation performance from CO2-based demand controlled ventilation: Simulation results from ASHRAE RP-1747 (ASHRAE RP-1747) , 2019, Science and Technology for the Built Environment.

[15]  ChangKyoo Yoo,et al.  A deep reinforcement learning-based autonomous ventilation control system for smart indoor air quality management in a subway station , 2019, Energy and Buildings.

[16]  S. Schiavon,et al.  Effect of sensor position on the performance of CO2-based demand controlled ventilation , 2019, Energy and Buildings.

[17]  S. Schiavon,et al.  Personal CO2 cloud: laboratory measurements of metabolic CO2 inhalation zone concentration and dispersion in a typical office desk setting , 2019, Journal of Exposure Science & Environmental Epidemiology.

[18]  S. Emmerich,et al.  The Role of Carbon Dioxide in Ventilation and IAQ Evaluation: 40 years of AIVC , 2019 .

[19]  D. Johansson,et al.  Indoor air temperatures, CO2 concentrations and ventilation rates: Long-term measurements in newly built low-energy schools in Sweden , 2019, Journal of Building Engineering.

[20]  A. Klumpp,et al.  Towards Low Cost and Low Temperature Capacitive CO2 Sensors Based on Amine Functionalized Silica Nanoparticles , 2019, Nanomaterials.

[21]  B. Olesen,et al.  Capabilities and limitations of wireless CO2, temperature and relative humidity sensors , 2019, Building and Environment.

[22]  K. Ramamurthy,et al.  Assessment of CO2-based demand controlled ventilation requirement for a flexible work environment with ductless split air conditioners , 2019, Science and Technology for the Built Environment.

[23]  Hwataik Han,et al.  Smart Ventilation for Energy Conservation in Buildings , 2019, Evergreen.

[24]  Wenjian Cai,et al.  Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption , 2019, Applied Energy.

[25]  Mohammed Essaaidi,et al.  Context-driven monitoring and control of buildings ventilation systems using big data and Internet of Things–based technologies , 2018, J. Syst. Control. Eng..

[26]  Muhannad Delwati,et al.  Demand controlled ventilation (DCV) in school and office buildings: Lessons learnt from case studies , 2018, Energy and Buildings.

[27]  Massimiliano Scarpa,et al.  Performance optimization of a demand controlled ventilation system by long term monitoring , 2018, Energy and Buildings.

[28]  Dirk Saelens,et al.  Implementation and verification of the IDEAS building energy simulation library , 2018 .

[29]  Massimiliano Scarpa,et al.  CO 2 based ventilation control in energy retrofit: An experimental assessment , 2018 .

[30]  Lieve Helsen,et al.  Simulation-Based Occupancy Estimation in Office Buildings Using CO2 Sensors , 2017, Building Simulation Conference Proceedings.

[31]  Hwataik Han,et al.  Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation , 2017 .

[32]  Massimiliano Scarpa,et al.  Annual Performance Monitoring of a Demand Controlled Ventilation System in a University Library , 2016 .

[33]  Brian J. Polidoro,et al.  Coupling the multizone airflow and contaminant transport software CONTAM with EnergyPlus using co-simulation , 2016, Building simulation.

[34]  Mohammed Essaaidi,et al.  A state-feedback approach for controlling ventilation systems in energy efficient buildings , 2015, 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC).

[35]  Josephine Lau,et al.  Demand-controlled ventilation for multiple-zone HVAC systems—Part 2: CO2-based dynamic reset with zone primary airflow minimum set-point reset (RP-1547) , 2015 .

[36]  Jan-Olof Dalenbäck,et al.  CO2 sensors for occupancy estimations: Potential in building automation applications , 2014 .

[37]  Josephine Lau,et al.  Demand controlled ventilation for multiple zone HVAC systems: CO2-based dynamic reset (RP 1547) , 2014 .

[38]  Kazuhide Ito,et al.  Integrated building energy computational fluid dynamics simulation for estimating the energy-saving effect of energy recovery ventilator with CO2 demand-controlled ventilation system in office space , 2014 .

[39]  Jong-Il Park,et al.  Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control , 2014 .

[40]  Igor Škrjanc,et al.  Control of indoor CO2 concentration based on a process model , 2014 .

[41]  Adams Rackes,et al.  Using multiobjective optimizations to discover dynamic building ventilation strategies that can improve indoor air quality and reduce energy use , 2014 .

[42]  ChangKyoo Yoo,et al.  Finding the optimal set points of a thermal and ventilation control system under changing outdoor weather conditions , 2014 .

[43]  Thierry S. Nouidui,et al.  Modelica Buildings library , 2014 .

[44]  Jin Wen,et al.  Stability and accuracy of variable air volume box control at low flows. Part 1: Laboratory test setup and variable air volume sensor test , 2014 .

[45]  Jin Wen,et al.  Stability and accuracy of variable air volume box control at low flows. Part 2: Controller test, system test, and field test , 2014 .

[46]  Cheng-Xian Lin,et al.  CO2 and thermal gradient based demand-driven stratified ventilation—Experimental and simulation study , 2013 .

[47]  Tao Lu,et al.  A new method for controlling CO2 in buildings with unscheduled opening hours , 2013 .

[48]  Yongjun Sun,et al.  Development and In-situ validation of a multi-zone demand-controlled ventilation strategy using a limited number of sensors , 2012 .

[49]  Nabil Nassif,et al.  A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems , 2012 .

[50]  Yin Hang,et al.  CO2-based demand controlled ventilation under new ASHRAE Standard 62.1-2010: a case study for a gymnasium of an elementary school at West Lafayette, Indiana , 2011 .

[51]  Tao Lu,et al.  A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings , 2011 .

[52]  Norhayati Mahyuddin,et al.  The spatial distribution of carbon dioxide in an environmental test chamber , 2010 .

[53]  William J. Fisk,et al.  Assessment of energy savings potential from the use of demand controlled ventilation in general office spaces in California , 2010 .

[54]  Zhigang Shi,et al.  Direct feedback linearization based control of CO2 demand controlled ventilation , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[55]  James E. Braun,et al.  Evaluation of simplified models for predicting CO2 concentrations in small commercial buildings , 2006 .

[56]  Kwok-Wai Mui,et al.  Pilot Study for the Performance of a New Demand Control Ventilation System in Hong Kong , 2005 .

[57]  Hilding Elmqvist,et al.  Physical system modeling with Modelica , 1998 .

[58]  Xiaohui Zhou,et al.  ENERGY AND VENTILATION PERFORMANCE ANALYSIS FOR CO2-BASED DEMAND-CONTROLLED VENTILATION IN MULTIPLE ZONE VAV SYSTEMS WITH MULTIPLE RECIRCULATION PATHS , 2020 .

[59]  Jili Zhang,et al.  An optimal control method for discrete variable outdoor air volume setpoint determination in variable air volume systems , 2020 .

[60]  A. Melikov,et al.  Validity of CO2 based ventilation design , 2019, E3S Web of Conferences.

[61]  Adélio Rodrigues Gaspar,et al.  DEVELOPMENT OF A NEW CO2-BASED DEMAND-CONTROLLED VENTILATION STRATEGY USING ENERGYPLUS , 2017 .

[62]  Stanley A. Mumma,et al.  Using Carbon Dioxide Measurements to Deter,mine Occupancy for Ventilation Controls , 2014 .

[63]  A. Koukam,et al.  Managing Ventilation Systems for Improving User Comfort in Smart Buildings using Reinforcement Learning Agents , 2014 .

[64]  Kazuhide Ito,et al.  Field-based study on the energy-saving effects of CO2 demand controlled ventilation in an office with application of Energy recovery ventilators , 2014 .

[65]  S. T. Taylor,et al.  CO~2-Based DCV Using 62.1-2004 , 2006 .

[66]  Mike Schell,et al.  Demand Control Ventilation Using CO~2 , 2001 .

[67]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[68]  David Södergren,et al.  A CO2-controlled ventilation system , 1982 .